Drought and flood sudden change event evaluation method

Abstract

The invention discloses a drought and flood sudden change event assessment method, which comprises the following steps: firstly, collecting rainfall series data, runoff series data and soil water content series data of a target area in a to-be-assessed time period, unifying a time sequence and a scale, and respectively calculating standardized indexes of the rainfall series data, the runoff series data and the soil water content series data; secondly, constructing a Copula function according to the three standardized drought and flood indexes, selecting and fitting the optimal Copula function by adopting a minimum informatization criterion and a Bayesian information criterion, performing normal inverse transformation on the function to obtain a comprehensive drought and flood index, and calculating according to the comprehensive drought and flood index to obtain a standardized drought and flood sudden turning index; and finally, performing grade division on the value of the standardized drought and flood sudden turning index to complete evaluation of the drought and flood sudden turning event in the target area. According to the method, the problems of single consideration factor, non-uniform judgment standard and insufficient consideration of land hydrological process in evaluation of the drought and flood sudden change event in the prior art are solved, and improvement of a drought and flood system evaluation system and risk identification analysis of the drought and flood sudden change event are facilitated.

Description

Technical Field

[0001] This invention belongs to the field of natural disaster risk assessment technology, specifically relating to the design of a method for assessing sudden shifts between drought and flood events. Background Technology

[0002] Climate change is profoundly reshaping the global water cycle through multi-dimensional mechanisms. The variability of key processes such as precipitation, runoff, and evapotranspiration within the system has increased, and the frequency and widespread occurrence of extreme hydrological events such as droughts and floods are becoming increasingly prominent. Drought-flood transition events refer to the coexistence or alternation of drought and flood events within a short period, i.e., a rapid change from one state to another. These mainly include two categories: drought-to-flood and flood-to-drought. The occurrence of drought-flood transition events is gradually becoming a new trend in my country's drought and flood disasters. Compared to drought or flood occurring alone, these combined events pose a greater threat to production, livelihoods, and disaster prevention and mitigation.

[0003] Currently, domestic and international research on the assessment of rapid drought-flood transition events mainly utilizes a series of hydrological and meteorological elements such as precipitation and runoff to construct drought-flood transition indicators at different scales, and conducts quantitative identification and assessment research on regional rapid drought-flood transition events. Existing technologies can be divided into two categories. One category uses traditional drought and flood indicators such as the Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Weighted Average Precipitation (SWAP), consecutive dry days, and percentage of precipitation anomaly. After calculating these indicator values, they are combined with run theory to identify the occurrence of abrupt drought-flood events. These indicators mainly consider precipitation as the primary factor. The other category uses indicators that directly identify abrupt drought-flood events. These indicators are represented by the long-period drought-flood indicator LDFAI and the short-period drought-flood indicator SDFAI. Based on these, the diurnal drought-flood indicator DDFAI has been derived. Currently, there are also indicators that are improved or innovated based on DDFAI, such as incorporating soil water content into the evaluation factors, resulting in the innovative multi-factor drought-flood indicator MSDFI. However, these indicators may have problems such as parameter differences, inconsistent judgment criteria with drought and flood standards, and insufficient consideration of land surface hydrological processes. Summary of the Invention

[0004] The purpose of this invention is to propose a method for assessing sudden drought-flood transition events. This method comprehensively assesses sudden drought-flood transition events in a region by considering land surface hydrological processes. It helps to ensure that the classification of sudden drought-flood transition levels is consistent with conventional drought-flood level classifications in terms of threshold settings and qualitative characteristics. This provides technical support for improving the drought-flood system assessment system and the risk identification and analysis of sudden drought-flood transition events.

[0005] The technical solution of this invention is: a method for assessing sudden shifts between drought and flood, comprising the following steps: S1. Collect precipitation, runoff, and soil moisture data for the target area during the time period to be evaluated.

[0006] S2. Calculate the first standardized precipitation index based on precipitation series data, calculate the second standardized precipitation index based on runoff series data, and calculate the standardized soil moisture index based on soil moisture content series data.

[0007] S3. Construct the Copula function based on the first standardized precipitation index, the second standardized precipitation index, and the standardized soil moisture index.

[0008] S4. Obtain the comprehensive drought and flood index based on the Copula function.

[0009] S5. The standardized drought-flood transition index is calculated based on the comprehensive drought-flood index.

[0010] S6. Classify the values ​​of the standardized drought-flood transition index into levels to complete the assessment of drought-flood transition events in the target area.

[0011] Furthermore, the formula for calculating the first standardized precipitation index based on precipitation series data in step S2 is as follows: in Indicates the first standardized precipitation index, This represents the probability of precipitation distribution. This indicates the amount of precipitation obtained from a precipitation series. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

[0012] Furthermore, the formula for calculating the second standardized precipitation index based on runoff series data in step S2 is as follows: in Indicates the second standardized precipitation index, This represents the probability of precipitation distribution. This represents the runoff volume obtained from the runoff series data. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

[0013] Furthermore, the formula for calculating the standardized soil moisture index based on the soil moisture content series data in step S2 is as follows: in Indicates the standardized soil moisture index, This represents the soil moisture value at a specific time scale within a soil moisture content series. This represents the average soil moisture content over multiple years on a time scale within a soil moisture content series data set. It represents the standard deviation of soil moisture over many years on a time scale in a series of soil moisture content data.

[0014] Furthermore, the Copula functions in step S3 include the Clayton-Copula function, the Frank-Copula function, the Gumbel-Copula function, the t-Copula function, and the Gaussian-Copula function.

[0015] Clayton-Copula function Specifically: Frank-Copula function Specifically: Gumbel-Copula function Specifically: t-Copula function Specifically: Gaussian-Copula function Specifically: in This refers to the first indicator, namely the first standardized precipitation index. This refers to the second indicator, namely the second standardized precipitation index. This refers to the third indicator, namely the standardized soil moisture index. Indicates the dependency parameter. v Represents the degree of freedom parameter. d Representing dimension, Represents the correlation matrix. Represents the integral variable with respect to dimension. Represents the gamma distribution function. This represents the inverse normal transform. Describing the degrees of freedom as v The inverse cumulative distribution function of the univariate t-distribution.

[0016] Furthermore, step S4 includes the following sub-steps: S41. Based on the minimum information criterion and the Bayesian information criterion, the goodness-of-fit of the five Copula functions is tested, and the Copula function with the best fit is selected as the joint distribution function of the three indicators.

[0017] S42. The comprehensive drought and flood index is calculated based on the joint distribution function: in This represents the comprehensive drought and flood index. This represents the inverse normal transform. This represents the cumulative joint probability of the joint distribution function.

[0018] Furthermore, the formula for calculating the standardized drought-flood transition index based on the comprehensive drought-flood index in step S5 is as follows: in Indicates the standardized drought-flood transition index, Indicates thei The comprehensive drought and flood index at a given time point Indicates the i +1 composite drought and flood index at one time point , n This indicates the length of the time series for the period to be evaluated.

[0019] Furthermore, in step S6, the standardized drought-flood transition index is divided into 9 levels: extreme drought to flood, severe drought to flood, moderate drought to flood, mild drought to flood, normal, mild flood to drought, moderate flood to drought, severe flood to drought, and extreme flood to drought.

[0020] The beneficial effects of this invention are: (1) This invention can effectively assess the possibility of a sudden shift from drought to flood in a certain region at a certain time. It can also predict the possibility and severity of the risk of a sudden shift from drought to flood in the region under different future scenarios based on future simulation and prediction data, thereby providing a scientific basis for drought and flood risk prevention and management decisions.

[0021] (2) This invention takes into account multiple factors such as precipitation, runoff, and soil. By calculating the standardized index of the three factors separately, it is beneficial to comprehensively reflect the drought and flood situation in the region and highlight the role of slopes and river channels in regulating and buffering precipitation.

[0022] (3) By using different types of Copula functions to fit the three drought and flood indices—the first standardized precipitation index, the second standardized precipitation index, and the standardized soil moisture index—this invention helps to avoid blind model selection, enhance the robustness of the analysis, and deepen the understanding of the data dependency structure.

[0023] (4) This invention uses the minimum information criterion and the Bayesian information criterion to test the goodness of fit of the Copula function and select the Copula function with the best fit. This is beneficial to automatically and objectively identify the model that accurately describes the joint distribution characteristics of precipitation, runoff and soil water while maintaining model simplicity among multiple candidate models.

[0024] (5) This invention quantifies the rapid shift between drought and flood by adopting a comprehensive drought and flood index that takes into account multiple factors such as precipitation, runoff, and soil, thereby avoiding misjudgment and omission of the rapid shift between drought and flood. It also ensures that the classification of the rapid shift between drought and flood is consistent with the conventional classification of drought and flood in terms of threshold setting and qualitative analysis, which is conducive to the unification of the drought and flood assessment system.

[0025] (6) By quantifying and classifying the indicators of rapid drought and flood, this invention helps to clarify the degree of rapid drought and flood in a region and provides support for decision-making on risk prevention and management of rapid drought and flood. Attached Figure Description

[0026] Figure 1The diagram shown is a flowchart of a method for assessing sudden shifts between drought and flood events provided by an embodiment of the present invention. Detailed Implementation

[0027] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0028] This invention provides a method for assessing sudden shifts between drought and flood events, such as... Figure 1 As shown, the process includes the following steps S1 to S6: S1. Collect precipitation, runoff, and soil moisture data for the target area during the time period to be evaluated.

[0029] Among them, precipitation data belongs to meteorological data, runoff data belongs to runoff data, and soil moisture data belongs to soil moisture data.

[0030] S2. Calculate the first standardized precipitation index based on precipitation series data, calculate the second standardized precipitation index based on runoff series data, and calculate the standardized soil moisture index based on soil moisture content series data.

[0031] In this embodiment of the invention, the formula for calculating the first standardized precipitation index based on precipitation series data is as follows: in Indicates the first standardized precipitation index, This represents the probability of precipitation distribution. This indicates the amount of precipitation obtained from a precipitation series. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

[0032] The formula for calculating the second standardized precipitation index based on runoff series data is as follows: in Indicates the second standardized precipitation index, This represents the probability of precipitation distribution. This represents the runoff volume obtained from the runoff series data. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

[0033] The formula for calculating the standardized soil moisture index based on soil moisture content series data is as follows: in Indicates the standardized soil moisture index, This represents the soil moisture value at a specific time scale within a soil moisture content series. This represents the average soil moisture content over multiple years on a time scale within a soil moisture content series data set. It represents the standard deviation of soil moisture over many years on a time scale in a series of soil moisture content data.

[0034] S3. Construct the Copula function based on the first standardized precipitation index, the second standardized precipitation index, and the standardized soil moisture index.

[0035] In this embodiment of the invention, the Copula function includes the Clayton-Copula function, the Frank-Copula function, the Gumbel-Copula function, the t-Copula function, and the Gaussian-Copula function.

[0036] Clayton-Copula function Specifically: Frank-Copula function Specifically: Gumbel-Copula function Specifically: t-Copula function Specifically: Gaussian-Copula function Specifically: in This refers to the first indicator, namely the first standardized precipitation index. This refers to the second indicator, namely the second standardized precipitation index. This refers to the third indicator, namely the standardized soil moisture index. Indicates the dependency parameter. v Represents the degree of freedom parameter. d Representing dimension, Represents the correlation matrix. Represents the integral variable with respect to dimension. Represents the gamma distribution function. This represents the inverse normal transform. Describing the degrees of freedom as v The inverse cumulative distribution function of the univariate t-distribution.

[0037] S4. Obtain the comprehensive drought and flood index based on the Copula function.

[0038] Step S4 includes the following sub-steps S41~S42: S41. Based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), a goodness-of-fit (GOF) test was performed on the five Copula functions, and the Copula function with the best fit was selected as the joint distribution function of the three indicators.

[0039] S42. The comprehensive drought and flood index is calculated based on the joint distribution function: in This represents the comprehensive drought and flood index. This represents the inverse normal transform. This represents the cumulative joint probability of the joint distribution function.

[0040] Step S4 primarily transforms the cumulative joint probability of the first standardized precipitation index, the second standardized precipitation index, and the standardized soil moisture index into values ​​with the same distribution as SPI, SRI, and SSMI. The SPRSMI index has the same statistical framework as SPEI, SRI, and SSMI, and can use the same threshold to classify drought levels, which is beneficial for comparing drought events at different regional and time scales.

[0041] S5. The standardized drought-flood transition index is calculated based on the comprehensive drought-flood index: in Indicates the standardized drought-flood transition index, Indicates the i The comprehensive drought and flood index at a given time point Indicates the i +1 composite drought and flood index at one time point , n This indicates the length of the time series for the period to be evaluated.

[0042] S6. Classify the values ​​of the standardized drought-flood transition index into levels to complete the assessment of drought-flood transition events in the target area.

[0043] In the embodiments of the present invention, the values of the standardized drought-flood rapid transition index are divided into 9 levels, namely extremely drought-to-flood (C_DFAI≥2), severely drought-to-flood (1.5≤C_DFAI<2), moderately drought-to-flood (1≤C_DFAI<1.5), slightly drought-to-flood (0.5<C_DFAI<1), normal (-0.5≤C_DFAI≤0.5), slightly flood-to-drought (-1<C_DFAI<-0.5), moderately flood-to-drought (-1.5<C_DFAI≤-1), severely flood-to-drought (-2<C_DFAI≤-1.5) and extremely flood-to-drought (C_DFAI≤-2).

[0044] Those of ordinary skill in the art will realize that the embodiments described herein are for helping the reader understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention according to the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A method for assessing sudden shifts between drought and flood events, characterized in that, The following steps are involved: S1. Collect precipitation, runoff, and soil moisture data for the target area during the time period to be evaluated. S2. Calculate the first standardized precipitation index based on precipitation series data, calculate the second standardized precipitation index based on runoff series data, and calculate the standardized soil moisture index based on soil moisture content series data. S3. Construct a Copula function based on the first standardized precipitation index, the second standardized precipitation index, and the standardized soil moisture index; S4. Obtain the comprehensive drought and flood index based on the Copula function; S5. The standardized drought-flood transition index is calculated based on the comprehensive drought-flood index. S6. Classify the values ​​of the standardized drought-flood transition index into levels to complete the assessment of drought-flood transition events in the target area.

2. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, The formula for calculating the first standardized precipitation index based on precipitation series data in step S2 is as follows: in Indicates the first standardized precipitation index, This represents the probability of precipitation distribution. This indicates the amount of precipitation obtained from a precipitation series. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

3. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, The formula for calculating the second standardized precipitation index based on runoff series data in step S2 is as follows: in Indicates the second standardized precipitation index, This represents the probability of precipitation distribution. This represents the runoff volume obtained from the runoff series data. Indicates positive and negative coefficients, when hour ,when hour , t Indicates intermediate variables. , , , , and Both are constants and , , , , , , Represents the gamma distribution function. This represents the shape parameter of the gamma distribution function. The scale parameter represents the gamma distribution function.

4. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, The formula for calculating the standardized soil moisture index based on soil moisture content series data in step S2 is as follows: in Indicates the standardized soil moisture index, This represents the soil moisture value at a specific time scale within a soil moisture content series. This represents the average soil moisture over many years at the time scale described in the soil moisture content series data. This represents the standard deviation of soil moisture over many years on the time scale described in the soil moisture content series data.

5. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, The Copula functions in step S3 include the Clayton-Copula function, the Frank-Copula function, the Gumbel-Copula function, the t-Copula function, and the Gaussian-Copula function; The Clayton-Copula function Specifically: The Frank-Copula function Specifically: The Gumbel-Copula function Specifically: The t-Copula function Specifically: The Gaussian-Copula function Specifically: in This refers to the first indicator, namely the first standardized precipitation index. This refers to the second indicator, namely the second standardized precipitation index. This refers to the third indicator, namely the standardized soil moisture index. Indicates the dependency parameter. v Represents the degree of freedom parameter. d Representing dimension, Represents the correlation matrix. Represents the integral variable with respect to dimension. Represents the gamma distribution function. This represents the inverse normal transform. Describing the degrees of freedom as v The inverse cumulative distribution function of the univariate t-distribution.

6. The method for assessing sudden shifts between drought and flood events according to claim 5, characterized in that, Step S4 includes the following sub-steps: S41. Based on the minimum information criterion and the Bayesian information criterion, the goodness-of-fit of the five Copula functions is tested, and the Copula function with the best fit is selected as the joint distribution function of the three indicators. S42. The comprehensive drought and flood index is calculated based on the joint distribution function: in This represents the comprehensive drought and flood index. This represents the inverse normal transform. This represents the cumulative joint probability of the joint distribution function.

7. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, The formula for calculating the standardized drought-flood transition index based on the comprehensive drought-flood index in step S5 is as follows: in Indicates the standardized drought-flood transition index, Indicates the first i The comprehensive drought and flood index at a given time point Indicates the first i +1 composite drought and flood index at one time point , n This indicates the length of the time series for the period to be evaluated.

8. The method for assessing sudden shifts between drought and flood events according to claim 1, characterized in that, In step S6, the standardized drought-flood transition index is divided into 9 levels: extreme drought to flood, severe drought to flood, moderate drought to flood, mild drought to flood, normal, mild flood to drought, moderate flood to drought, severe flood to drought, and extreme flood to drought.

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